Papers by Theodoros Rekatsinas
Unsupervised Relation Extraction from Language Models using Constrained Cloze Completion (2020.findings-emnlp)
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| Challenge: | Existing methods to extract relations from text use fine-tuned machine learning approaches. |
| Approach: | They introduce a framework that performs constrained cloze completion over pretrained language models to perform unsupervised relation extraction. |
| Outcome: | The proposed framework outperforms competing unsupervised relation extraction methods based on pretrained language models by 27.8 F1 points compared to the next-best method. |
Construction of Paired Knowledge Graph - Text Datasets Informed by Cyclic Evaluation (2024.lrec-main)
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Ali Mousavi, Xin Zhan, He Bai, Peng Shi, Theodoros Rekatsinas, Benjamin Han, Yunyao Li, Jeffrey Pound, Joshua M. Susskind, Natalie Schluter, Ihab F. Ilyas, Navdeep Jaitly
| Challenge: | Prior studies have shown that sequence-to-sequence models learn to hallucinate when the conditioning data has poor correlation with the sequence being produced. |
| Approach: | They construct a dataset that pairs Knowledge Graphs (KG) and text together and compare their results to a cyclic evaluation model. |
| Outcome: | The proposed model performs better on cyclic generation of KGs than on KG-T, but less well on synchronization of KTs. |